Overview

Dataset statistics

Number of variables15
Number of observations2175
Missing cells8049
Missing cells (%)24.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory255.0 KiB
Average record size in memory120.1 B

Variable types

Categorical6
Numeric9

Warnings

country has a high cardinality: 78 distinct values High cardinality
iso_code has a high cardinality: 73 distinct values High cardinality
date has a high cardinality: 53 distinct values High cardinality
source_website has a high cardinality: 74 distinct values High cardinality
total_vaccinations is highly correlated with people_vaccinated and 3 other fieldsHigh correlation
people_vaccinated is highly correlated with total_vaccinations and 3 other fieldsHigh correlation
people_fully_vaccinated is highly correlated with total_vaccinations and 2 other fieldsHigh correlation
daily_vaccinations_raw is highly correlated with total_vaccinations and 2 other fieldsHigh correlation
daily_vaccinations is highly correlated with total_vaccinations and 3 other fieldsHigh correlation
total_vaccinations_per_hundred is highly correlated with people_vaccinated_per_hundredHigh correlation
people_vaccinated_per_hundred is highly correlated with total_vaccinations_per_hundredHigh correlation
source_name is highly correlated with source_website and 2 other fieldsHigh correlation
source_website is highly correlated with source_name and 3 other fieldsHigh correlation
country is highly correlated with source_name and 3 other fieldsHigh correlation
iso_code is highly correlated with source_name and 3 other fieldsHigh correlation
vaccines is highly correlated with source_website and 2 other fieldsHigh correlation
iso_code has 210 (9.7%) missing values Missing
total_vaccinations has 738 (33.9%) missing values Missing
people_vaccinated has 1047 (48.1%) missing values Missing
people_fully_vaccinated has 1553 (71.4%) missing values Missing
daily_vaccinations_raw has 1007 (46.3%) missing values Missing
daily_vaccinations has 78 (3.6%) missing values Missing
total_vaccinations_per_hundred has 738 (33.9%) missing values Missing
people_vaccinated_per_hundred has 1047 (48.1%) missing values Missing
people_fully_vaccinated_per_hundred has 1553 (71.4%) missing values Missing
daily_vaccinations_per_million has 78 (3.6%) missing values Missing
total_vaccinations_per_hundred has 48 (2.2%) zeros Zeros
people_vaccinated_per_hundred has 25 (1.1%) zeros Zeros
people_fully_vaccinated_per_hundred has 39 (1.8%) zeros Zeros

Reproduction

Analysis started2021-02-05 19:19:54.297799
Analysis finished2021-02-05 19:20:08.204534
Duration13.91 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

country
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct78
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
Northern Ireland
 
52
Wales
 
52
Scotland
 
52
China
 
48
Canada
 
47
Other values (73)
1924 

Length

Max length20
Median length7
Mean length7.740229885
Min length4

Characters and Unicode

Total characters16835
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.3%

Sample

1st rowAlgeria
2nd rowAlgeria
3rd rowAndorra
4th rowAndorra
5th rowAndorra
ValueCountFrequency (%)
Northern Ireland52
 
2.4%
Wales52
 
2.4%
Scotland52
 
2.4%
China48
 
2.2%
Canada47
 
2.2%
Israel47
 
2.2%
United States46
 
2.1%
United Kingdom45
 
2.1%
England45
 
2.1%
Bahrain43
 
2.0%
Other values (68)1698
78.1%
2021-02-05T20:20:08.425362image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united121
 
4.9%
ireland84
 
3.4%
northern61
 
2.4%
wales52
 
2.1%
scotland52
 
2.1%
china48
 
1.9%
israel47
 
1.9%
canada47
 
1.9%
states46
 
1.8%
kingdom45
 
1.8%
Other values (76)1889
75.8%

Most occurring characters

ValueCountFrequency (%)
a2375
14.1%
n1441
 
8.6%
e1373
 
8.2%
i1251
 
7.4%
r1131
 
6.7%
l875
 
5.2%
t855
 
5.1%
d728
 
4.3%
o708
 
4.2%
s469
 
2.8%
Other values (36)5629
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14040
83.4%
Uppercase Letter2478
 
14.7%
Space Separator317
 
1.9%

Most frequent character per category

ValueCountFrequency (%)
a2375
16.9%
n1441
10.3%
e1373
9.8%
i1251
8.9%
r1131
 
8.1%
l875
 
6.2%
t855
 
6.1%
d728
 
5.2%
o708
 
5.0%
s469
 
3.3%
Other values (14)2834
20.2%
ValueCountFrequency (%)
S383
15.5%
C286
11.5%
I265
10.7%
B163
 
6.6%
N135
 
5.4%
A129
 
5.2%
E128
 
5.2%
M125
 
5.0%
U121
 
4.9%
L119
 
4.8%
Other values (11)624
25.2%
ValueCountFrequency (%)
317
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16518
98.1%
Common317
 
1.9%

Most frequent character per script

ValueCountFrequency (%)
a2375
14.4%
n1441
 
8.7%
e1373
 
8.3%
i1251
 
7.6%
r1131
 
6.8%
l875
 
5.3%
t855
 
5.2%
d728
 
4.4%
o708
 
4.3%
s469
 
2.8%
Other values (35)5312
32.2%
ValueCountFrequency (%)
317
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII16835
100.0%

Most frequent character per block

ValueCountFrequency (%)
a2375
14.1%
n1441
 
8.6%
e1373
 
8.2%
i1251
 
7.4%
r1131
 
6.7%
l875
 
5.2%
t855
 
5.1%
d728
 
4.3%
o708
 
4.2%
s469
 
2.8%
Other values (36)5629
33.4%

iso_code
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)3.7%
Missing210
Missing (%)9.7%
Memory size17.1 KiB
CHN
 
48
ISR
 
47
CAN
 
47
USA
 
46
GBR
 
45
Other values (68)
1732 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5895
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.3%

Sample

1st rowDZA
2nd rowDZA
3rd rowAND
4th rowAND
5th rowAND
ValueCountFrequency (%)
CHN48
 
2.2%
ISR47
 
2.2%
CAN47
 
2.2%
USA46
 
2.1%
GBR45
 
2.1%
BHR43
 
2.0%
CHL42
 
1.9%
MEX42
 
1.9%
CHE40
 
1.8%
CRI40
 
1.8%
Other values (63)1525
70.1%
(Missing)210
 
9.7%
2021-02-05T20:20:08.648030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chn48
 
2.4%
can47
 
2.4%
isr47
 
2.4%
usa46
 
2.3%
gbr45
 
2.3%
bhr43
 
2.2%
mex42
 
2.1%
chl42
 
2.1%
che40
 
2.0%
cri40
 
2.0%
Other values (63)1525
77.6%

Most occurring characters

ValueCountFrequency (%)
R646
 
11.0%
S435
 
7.4%
N423
 
7.2%
L378
 
6.4%
A376
 
6.4%
U370
 
6.3%
C368
 
6.2%
E349
 
5.9%
I310
 
5.3%
T269
 
4.6%
Other values (15)1971
33.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5895
100.0%

Most frequent character per category

ValueCountFrequency (%)
R646
 
11.0%
S435
 
7.4%
N423
 
7.2%
L378
 
6.4%
A376
 
6.4%
U370
 
6.3%
C368
 
6.2%
E349
 
5.9%
I310
 
5.3%
T269
 
4.6%
Other values (15)1971
33.4%

Most occurring scripts

ValueCountFrequency (%)
Latin5895
100.0%

Most frequent character per script

ValueCountFrequency (%)
R646
 
11.0%
S435
 
7.4%
N423
 
7.2%
L378
 
6.4%
A376
 
6.4%
U370
 
6.3%
C368
 
6.2%
E349
 
5.9%
I310
 
5.3%
T269
 
4.6%
Other values (15)1971
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5895
100.0%

Most frequent character per block

ValueCountFrequency (%)
R646
 
11.0%
S435
 
7.4%
N423
 
7.2%
L378
 
6.4%
A376
 
6.4%
U370
 
6.3%
C368
 
6.2%
E349
 
5.9%
I310
 
5.3%
T269
 
4.6%
Other values (15)1971
33.4%

date
Categorical

HIGH CARDINALITY

Distinct53
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
2021-01-27
 
64
2021-01-26
 
64
2021-01-29
 
63
2021-01-28
 
63
2021-01-24
 
63
Other values (48)
1858 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters21750
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-29
2nd row2021-01-30
3rd row2021-01-25
4th row2021-01-26
5th row2021-01-27
ValueCountFrequency (%)
2021-01-2764
 
2.9%
2021-01-2664
 
2.9%
2021-01-2963
 
2.9%
2021-01-2863
 
2.9%
2021-01-2463
 
2.9%
2021-01-2261
 
2.8%
2021-01-2561
 
2.8%
2021-01-2161
 
2.8%
2021-01-2360
 
2.8%
2021-01-3060
 
2.8%
Other values (43)1555
71.5%
2021-02-05T20:20:08.906116image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-01-2764
 
2.9%
2021-01-2664
 
2.9%
2021-01-2963
 
2.9%
2021-01-2863
 
2.9%
2021-01-2463
 
2.9%
2021-01-2261
 
2.8%
2021-01-2561
 
2.8%
2021-01-2161
 
2.8%
2021-01-2360
 
2.8%
2021-01-3060
 
2.8%
Other values (43)1555
71.5%

Most occurring characters

ValueCountFrequency (%)
25820
26.8%
05136
23.6%
14838
22.2%
-4350
20.0%
3414
 
1.9%
9217
 
1.0%
8212
 
1.0%
7201
 
0.9%
6194
 
0.9%
5185
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17400
80.0%
Dash Punctuation4350
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
25820
33.4%
05136
29.5%
14838
27.8%
3414
 
2.4%
9217
 
1.2%
8212
 
1.2%
7201
 
1.2%
6194
 
1.1%
5185
 
1.1%
4183
 
1.1%
ValueCountFrequency (%)
-4350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common21750
100.0%

Most frequent character per script

ValueCountFrequency (%)
25820
26.8%
05136
23.6%
14838
22.2%
-4350
20.0%
3414
 
1.9%
9217
 
1.0%
8212
 
1.0%
7201
 
0.9%
6194
 
0.9%
5185
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII21750
100.0%

Most frequent character per block

ValueCountFrequency (%)
25820
26.8%
05136
23.6%
14838
22.2%
-4350
20.0%
3414
 
1.9%
9217
 
1.0%
8212
 
1.0%
7201
 
0.9%
6194
 
0.9%
5185
 
0.9%

total_vaccinations
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1403
Distinct (%)97.6%
Missing738
Missing (%)33.9%
Infinite0
Infinite (%)0.0%
Mean960177.2846
Minimum0
Maximum33878254
Zeros21
Zeros (%)1.0%
Memory size17.1 KiB
2021-02-05T20:20:09.002456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1715.4
Q121169
median112213
Q3495164
95-th percentile4374347.2
Maximum33878254
Range33878254
Interquartile range (IQR)473995

Descriptive statistics

Standard deviation3134586.609
Coefficient of variation (CV)3.264591507
Kurtosis52.68255573
Mean960177.2846
Median Absolute Deviation (MAD)105204
Skewness6.690929966
Sum1379774758
Variance9.825633207 × 1012
MonotocityNot monotonic
2021-02-05T20:20:09.119808image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021
 
1.0%
21743
 
0.1%
1000002
 
0.1%
128662
 
0.1%
58472
 
0.1%
70172
 
0.1%
150002
 
0.1%
20002
 
0.1%
993662
 
0.1%
300002
 
0.1%
Other values (1393)1397
64.2%
(Missing)738
33.9%
ValueCountFrequency (%)
021
1.0%
51
 
< 0.1%
61
 
< 0.1%
131
 
< 0.1%
261
 
< 0.1%
ValueCountFrequency (%)
338782541
< 0.1%
327808601
< 0.1%
322224021
< 0.1%
311232991
< 0.1%
295779021
< 0.1%

people_vaccinated
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1104
Distinct (%)97.9%
Missing1047
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean996184.6356
Minimum0
Maximum27154956
Zeros4
Zeros (%)0.2%
Memory size17.1 KiB
2021-02-05T20:20:09.238476image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2586.45
Q126310.5
median131923
Q3567644.5
95-th percentile4593472
Maximum27154956
Range27154956
Interquartile range (IQR)541334

Descriptive statistics

Standard deviation3002410.852
Coefficient of variation (CV)3.013910017
Kurtosis37.1187987
Mean996184.6356
Median Absolute Deviation (MAD)119614.5
Skewness5.698532561
Sum1123696269
Variance9.014470926 × 1012
MonotocityNot monotonic
2021-02-05T20:20:09.362136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2477310
 
0.5%
04
 
0.2%
21743
 
0.1%
110732
 
0.1%
70172
 
0.1%
567642
 
0.1%
457072
 
0.1%
128662
 
0.1%
58472
 
0.1%
117432
 
0.1%
Other values (1094)1097
50.4%
(Missing)1047
48.1%
ValueCountFrequency (%)
04
0.2%
51
 
< 0.1%
131
 
< 0.1%
261
 
< 0.1%
521
 
< 0.1%
ValueCountFrequency (%)
271549561
< 0.1%
264408361
< 0.1%
260231531
< 0.1%
252011431
< 0.1%
240641651
< 0.1%

people_fully_vaccinated
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct571
Distinct (%)91.8%
Missing1553
Missing (%)71.4%
Infinite0
Infinite (%)0.0%
Mean226682.8746
Minimum1
Maximum6436931
Zeros0
Zeros (%)0.0%
Memory size17.1 KiB
2021-02-05T20:20:09.478662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile314.9
Q14537.25
median17929
Q3107794.5
95-th percentile877724.05
Maximum6436931
Range6436930
Interquartile range (IQR)103257.25

Descriptive statistics

Standard deviation714811.6473
Coefficient of variation (CV)3.153355315
Kurtosis37.88514871
Mean226682.8746
Median Absolute Deviation (MAD)16242
Skewness5.770907329
Sum140996748
Variance5.109556912 × 1011
MonotocityNot monotonic
2021-02-05T20:20:09.586490image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25000024
 
1.1%
7504
 
0.2%
19194
 
0.2%
23
 
0.1%
313143
 
0.1%
26693
 
0.1%
83603
 
0.1%
92023
 
0.1%
83643
 
0.1%
7122
 
0.1%
Other values (561)570
 
26.2%
(Missing)1553
71.4%
ValueCountFrequency (%)
11
 
< 0.1%
23
0.1%
52
0.1%
61
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
64369311
< 0.1%
60647921
< 0.1%
59278471
< 0.1%
56571421
< 0.1%
52596931
< 0.1%

daily_vaccinations_raw
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1114
Distinct (%)95.4%
Missing1007
Missing (%)46.3%
Infinite0
Infinite (%)0.0%
Mean61725.84075
Minimum0
Maximum1693241
Zeros6
Zeros (%)0.3%
Memory size17.1 KiB
2021-02-05T20:20:09.704358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile174.6
Q11798
median9541.5
Q350325
95-th percentile286385.35
Maximum1693241
Range1693241
Interquartile range (IQR)48527

Descriptive statistics

Standard deviation169066.3617
Coefficient of variation (CV)2.738988398
Kurtosis43.04503162
Mean61725.84075
Median Absolute Deviation (MAD)8830
Skewness5.954800288
Sum72095782
Variance2.858343465 × 1010
MonotocityNot monotonic
2021-02-05T20:20:09.980475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06
 
0.3%
5345
 
0.2%
14
 
0.2%
30003
 
0.1%
9303
 
0.1%
201403
 
0.1%
9243
 
0.1%
83
 
0.1%
11633
 
0.1%
6042
 
0.1%
Other values (1104)1133
52.1%
(Missing)1007
46.3%
ValueCountFrequency (%)
06
0.3%
14
0.2%
31
 
< 0.1%
42
 
0.1%
62
 
0.1%
ValueCountFrequency (%)
16932411
< 0.1%
16909791
< 0.1%
15615851
< 0.1%
15453971
< 0.1%
15410481
< 0.1%

daily_vaccinations
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1734
Distinct (%)82.7%
Missing78
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean50159.77682
Minimum1
Maximum1355451
Zeros0
Zeros (%)0.0%
Memory size17.1 KiB
2021-02-05T20:20:10.106774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile224
Q11358
median5872
Q324625
95-th percentile257204.6
Maximum1355451
Range1355450
Interquartile range (IQR)23267

Descriptive statistics

Standard deviation145797.9113
Coefficient of variation (CV)2.906669857
Kurtosis34.90511203
Mean50159.77682
Median Absolute Deviation (MAD)5242
Skewness5.462772433
Sum105185052
Variance2.125703095 × 1010
MonotocityNot monotonic
2021-02-05T20:20:10.233369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112130
 
1.4%
300022
 
1.0%
6321
 
1.0%
12619
 
0.9%
18750016
 
0.7%
6214
 
0.6%
24011
 
0.5%
1320010
 
0.5%
33578
 
0.4%
21998
 
0.4%
Other values (1724)1938
89.1%
(Missing)78
 
3.6%
ValueCountFrequency (%)
11
< 0.1%
61
< 0.1%
261
< 0.1%
301
< 0.1%
361
< 0.1%
ValueCountFrequency (%)
13554511
< 0.1%
13249491
< 0.1%
13199811
< 0.1%
13179461
< 0.1%
12914161
< 0.1%

total_vaccinations_per_hundred
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct592
Distinct (%)41.2%
Missing738
Missing (%)33.9%
Infinite0
Infinite (%)0.0%
Mean3.815351427
Minimum0
Maximum60.14
Zeros48
Zeros (%)2.2%
Memory size17.1 KiB
2021-02-05T20:20:10.358611image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.018
Q10.36
median1.37
Q33.24
95-th percentile17.214
Maximum60.14
Range60.14
Interquartile range (IQR)2.88

Descriptive statistics

Standard deviation7.500090257
Coefficient of variation (CV)1.965766562
Kurtosis19.25180801
Mean3.815351427
Median Absolute Deviation (MAD)1.17
Skewness4.022725824
Sum5482.66
Variance56.25135386
MonotocityNot monotonic
2021-02-05T20:20:10.475882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
048
 
2.2%
0.0124
 
1.1%
0.0516
 
0.7%
0.0415
 
0.7%
0.0314
 
0.6%
0.0214
 
0.6%
0.0612
 
0.6%
0.1911
 
0.5%
0.0711
 
0.5%
0.111
 
0.5%
Other values (582)1261
58.0%
(Missing)738
33.9%
ValueCountFrequency (%)
048
2.2%
0.0124
1.1%
0.0214
 
0.6%
0.0314
 
0.6%
0.0415
 
0.7%
ValueCountFrequency (%)
60.141
< 0.1%
58.91
< 0.1%
57.741
< 0.1%
56.391
< 0.1%
54.871
< 0.1%

people_vaccinated_per_hundred
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct523
Distinct (%)46.4%
Missing1047
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean3.86233156
Minimum0
Maximum38.8
Zeros25
Zeros (%)1.1%
Memory size17.1 KiB
2021-02-05T20:20:10.601266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q10.4275
median1.58
Q33.18
95-th percentile19.243
Maximum38.8
Range38.8
Interquartile range (IQR)2.7525

Descriptive statistics

Standard deviation6.711845953
Coefficient of variation (CV)1.737770528
Kurtosis10.01915225
Mean3.86233156
Median Absolute Deviation (MAD)1.22
Skewness3.08606387
Sum4356.71
Variance45.0488761
MonotocityNot monotonic
2021-02-05T20:20:10.720355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
025
 
1.1%
0.0120
 
0.9%
0.4915
 
0.7%
0.0712
 
0.6%
0.311
 
0.5%
0.0511
 
0.5%
0.3110
 
0.5%
0.1310
 
0.5%
0.0410
 
0.5%
0.039
 
0.4%
Other values (513)995
45.7%
(Missing)1047
48.1%
ValueCountFrequency (%)
025
1.1%
0.0120
0.9%
0.029
 
0.4%
0.039
 
0.4%
0.0410
 
0.5%
ValueCountFrequency (%)
38.81
< 0.1%
38.511
< 0.1%
38.251
< 0.1%
38.192
0.1%
38.111
< 0.1%

people_fully_vaccinated_per_hundred
Real number (ℝ≥0)

MISSING
ZEROS

Distinct153
Distinct (%)24.6%
Missing1553
Missing (%)71.4%
Infinite0
Infinite (%)0.0%
Mean0.899244373
Minimum0
Maximum22.03
Zeros39
Zeros (%)1.8%
Memory size17.1 KiB
2021-02-05T20:20:10.844016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.235
Q30.7575
95-th percentile2.53
Maximum22.03
Range22.03
Interquartile range (IQR)0.7075

Descriptive statistics

Standard deviation2.645650076
Coefficient of variation (CV)2.942081324
Kurtosis39.55739567
Mean0.899244373
Median Absolute Deviation (MAD)0.225
Skewness6.093568272
Sum559.33
Variance6.999464323
MonotocityNot monotonic
2021-02-05T20:20:10.965090image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
039
 
1.8%
0.0134
 
1.6%
0.0233
 
1.5%
0.0429
 
1.3%
2.5324
 
1.1%
0.0524
 
1.1%
0.0721
 
1.0%
0.0315
 
0.7%
0.114
 
0.6%
0.7811
 
0.5%
Other values (143)378
 
17.4%
(Missing)1553
71.4%
ValueCountFrequency (%)
039
1.8%
0.0134
1.6%
0.0233
1.5%
0.0315
 
0.7%
0.0429
1.3%
ValueCountFrequency (%)
22.031
< 0.1%
21.471
< 0.1%
21.131
< 0.1%
20.741
< 0.1%
20.071
< 0.1%

daily_vaccinations_per_million
Real number (ℝ≥0)

MISSING

Distinct1326
Distinct (%)63.2%
Missing78
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean1777.1464
Minimum0
Maximum30869
Zeros2
Zeros (%)0.1%
Memory size17.1 KiB
2021-02-05T20:20:11.079611image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48
Q1298
median797
Q31431
95-th percentile6606.2
Maximum30869
Range30869
Interquartile range (IQR)1133

Descriptive statistics

Standard deviation3388.462636
Coefficient of variation (CV)1.906687393
Kurtosis22.48705863
Mean1777.1464
Median Absolute Deviation (MAD)535
Skewness4.352071836
Sum3726676
Variance11481679.04
MonotocityNot monotonic
2021-02-05T20:20:11.189042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26231
 
1.4%
34725
 
1.1%
18522
 
1.0%
321120
 
0.9%
13016
 
0.7%
4813
 
0.6%
9912
 
0.6%
4710
 
0.5%
110
 
0.5%
689
 
0.4%
Other values (1316)1929
88.7%
(Missing)78
 
3.6%
ValueCountFrequency (%)
02
 
0.1%
110
0.5%
27
0.3%
33
 
0.1%
41
 
< 0.1%
ValueCountFrequency (%)
308691
< 0.1%
304241
< 0.1%
292661
< 0.1%
284651
< 0.1%
279901
< 0.1%

vaccines
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
Pfizer/BioNTech
1019 
Moderna, Pfizer/BioNTech
543 
Oxford/AstraZeneca, Pfizer/BioNTech
246 
Pfizer/BioNTech, Sinopharm
 
73
Sputnik V
 
69
Other values (9)
225 

Length

Max length37
Median length15
Mean length20.19494253
Min length7

Characters and Unicode

Total characters43924
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSputnik V
2nd rowSputnik V
3rd rowPfizer/BioNTech
4th rowPfizer/BioNTech
5th rowPfizer/BioNTech
ValueCountFrequency (%)
Pfizer/BioNTech1019
46.9%
Moderna, Pfizer/BioNTech543
25.0%
Oxford/AstraZeneca, Pfizer/BioNTech246
 
11.3%
Pfizer/BioNTech, Sinopharm73
 
3.4%
Sputnik V69
 
3.2%
CNBG, Sinovac48
 
2.2%
Sinovac45
 
2.1%
Oxford/AstraZeneca, Sinopharm28
 
1.3%
Pfizer/BioNTech, Sinopharm, Sputnik V27
 
1.2%
Oxford/AstraZeneca22
 
1.0%
Other values (4)55
 
2.5%
2021-02-05T20:20:11.388119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pfizer/biontech1917
57.9%
moderna543
 
16.4%
oxford/astrazeneca335
 
10.1%
sinopharm135
 
4.1%
sinovac121
 
3.7%
v96
 
2.9%
sputnik96
 
2.9%
cnbg48
 
1.4%
covaxin20
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e5047
 
11.5%
i4206
 
9.6%
r3265
 
7.4%
o3071
 
7.0%
c2373
 
5.4%
f2252
 
5.1%
/2252
 
5.1%
h2052
 
4.7%
B1965
 
4.5%
N1965
 
4.5%
Other values (24)15476
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter29620
67.4%
Uppercase Letter9876
 
22.5%
Other Punctuation3292
 
7.5%
Space Separator1136
 
2.6%

Most frequent character per category

ValueCountFrequency (%)
e5047
17.0%
i4206
14.2%
r3265
11.0%
o3071
10.4%
c2373
8.0%
f2252
7.6%
h2052
6.9%
z1917
 
6.5%
a1489
 
5.0%
n1250
 
4.2%
Other values (9)2698
9.1%
ValueCountFrequency (%)
B1965
19.9%
N1965
19.9%
P1917
19.4%
T1917
19.4%
M543
 
5.5%
S352
 
3.6%
O335
 
3.4%
A335
 
3.4%
Z335
 
3.4%
V96
 
1.0%
Other values (2)116
 
1.2%
ValueCountFrequency (%)
/2252
68.4%
,1040
31.6%
ValueCountFrequency (%)
1136
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39496
89.9%
Common4428
 
10.1%

Most frequent character per script

ValueCountFrequency (%)
e5047
12.8%
i4206
 
10.6%
r3265
 
8.3%
o3071
 
7.8%
c2373
 
6.0%
f2252
 
5.7%
h2052
 
5.2%
B1965
 
5.0%
N1965
 
5.0%
P1917
 
4.9%
Other values (21)11383
28.8%
ValueCountFrequency (%)
/2252
50.9%
1136
25.7%
,1040
23.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII43924
100.0%

Most frequent character per block

ValueCountFrequency (%)
e5047
 
11.5%
i4206
 
9.6%
r3265
 
7.4%
o3071
 
7.0%
c2373
 
5.4%
f2252
 
5.1%
/2252
 
5.1%
h2052
 
4.7%
B1965
 
4.5%
N1965
 
4.5%
Other values (24)15476
35.2%

source_name
Categorical

HIGH CORRELATION

Distinct47
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
Ministry of Health
645 
Government of the United Kingdom
246 
National Health Commission
 
88
National Health Service
 
76
Government of Israel
 
47
Other values (42)
1073 

Length

Max length59
Median length21
Mean length25.94804598
Min length9

Characters and Unicode

Total characters56437
Distinct characters49
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.3%

Sample

1st rowMinistry of Health
2nd rowMinistry of Health
3rd rowGovernment of Andorra
4th rowGovernment of Andorra
5th rowGovernment of Andorra
ValueCountFrequency (%)
Ministry of Health645
29.7%
Government of the United Kingdom246
 
11.3%
National Health Commission88
 
4.0%
National Health Service76
 
3.5%
Government of Israel47
 
2.2%
Government of Canada47
 
2.2%
Centers for Disease Control and Prevention46
 
2.1%
Secretary of Health42
 
1.9%
Department of Statistics and Health Information42
 
1.9%
Federal Office of Public Health40
 
1.8%
Other values (37)856
39.4%
2021-02-05T20:20:11.626313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of1470
17.9%
health1255
15.3%
ministry645
 
7.9%
government545
 
6.6%
national320
 
3.9%
the317
 
3.9%
kingdom246
 
3.0%
united246
 
3.0%
public223
 
2.7%
and182
 
2.2%
Other values (80)2751
33.5%

Most occurring characters

ValueCountFrequency (%)
6025
 
10.7%
e5183
 
9.2%
t5080
 
9.0%
n4545
 
8.1%
i4116
 
7.3%
o3913
 
6.9%
a3502
 
6.2%
r2885
 
5.1%
l2205
 
3.9%
f1841
 
3.3%
Other values (39)17142
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter44196
78.3%
Space Separator6025
 
10.7%
Uppercase Letter5994
 
10.6%
Decimal Number122
 
0.2%
Dash Punctuation61
 
0.1%
Other Punctuation39
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e5183
11.7%
t5080
11.5%
n4545
10.3%
i4116
9.3%
o3913
8.9%
a3502
 
7.9%
r2885
 
6.5%
l2205
 
5.0%
f1841
 
4.2%
s1812
 
4.1%
Other values (13)9114
20.6%
ValueCountFrequency (%)
H1326
22.1%
M691
11.5%
G574
9.6%
S443
 
7.4%
I418
 
7.0%
C385
 
6.4%
N365
 
6.1%
P314
 
5.2%
K284
 
4.7%
U246
 
4.1%
Other values (11)948
15.8%
ValueCountFrequency (%)
161
50.0%
961
50.0%
ValueCountFrequency (%)
6025
100.0%
ValueCountFrequency (%)
-61
100.0%
ValueCountFrequency (%)
,39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin50190
88.9%
Common6247
 
11.1%

Most frequent character per script

ValueCountFrequency (%)
e5183
 
10.3%
t5080
 
10.1%
n4545
 
9.1%
i4116
 
8.2%
o3913
 
7.8%
a3502
 
7.0%
r2885
 
5.7%
l2205
 
4.4%
f1841
 
3.7%
s1812
 
3.6%
Other values (34)15108
30.1%
ValueCountFrequency (%)
6025
96.4%
-61
 
1.0%
161
 
1.0%
961
 
1.0%
,39
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII56437
100.0%

Most frequent character per block

ValueCountFrequency (%)
6025
 
10.7%
e5183
 
9.2%
t5080
 
9.0%
n4545
 
8.1%
i4116
 
7.3%
o3913
 
6.9%
a3502
 
6.2%
r2885
 
5.1%
l2205
 
3.9%
f1841
 
3.3%
Other values (39)17142
30.4%

source_website
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct74
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size17.1 KiB
https://coronavirus.data.gov.uk/details/healthcare
246 
http://english.www.gov.cn/news/topnews/202101/31/content_WS6016a596c6d0f72576944df7.html
 
48
https://datadashboard.health.gov.il/COVID-19/general
 
47
https://health-infobase.canada.ca/covid-19/vaccination-coverage/
 
47
https://covid.cdc.gov/covid-data-tracker/#vaccinations
 
46
Other values (69)
1741 

Length

Max length211
Median length65
Mean length74.86804598
Min length20

Characters and Unicode

Total characters162838
Distinct characters69
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.3%

Sample

1st rowhttps://www.aps.dz/regions/116777-blida-covid-19-trente-vaccines-au-matin-du-1er-jour-de-la-campagne
2nd rowhttps://www.aps.dz/regions/116777-blida-covid-19-trente-vaccines-au-matin-du-1er-jour-de-la-campagne
3rd rowhttps://www.govern.ad/comunicats/item/12379-se-supera-el-miler-de-persones-vacunes-contra-el-coronavirus-sars-cov-2-a-andorra
4th rowhttps://www.govern.ad/comunicats/item/12379-se-supera-el-miler-de-persones-vacunes-contra-el-coronavirus-sars-cov-2-a-andorra
5th rowhttps://www.govern.ad/comunicats/item/12379-se-supera-el-miler-de-persones-vacunes-contra-el-coronavirus-sars-cov-2-a-andorra
ValueCountFrequency (%)
https://coronavirus.data.gov.uk/details/healthcare246
 
11.3%
http://english.www.gov.cn/news/topnews/202101/31/content_WS6016a596c6d0f72576944df7.html48
 
2.2%
https://datadashboard.health.gov.il/COVID-19/general47
 
2.2%
https://health-infobase.canada.ca/covid-19/vaccination-coverage/47
 
2.2%
https://covid.cdc.gov/covid-data-tracker/#vaccinations46
 
2.1%
https://twitter.com/MOH_Bahrain/status/135707146012951756843
 
2.0%
https://informesdeis.minsal.cl/SASVisualAnalytics/?reportUri=%2Freports%2Freports%2F1a8cc7ff-7df0-474f-a147-929ee45d1900&sectionIndex=0&sso_guest=true&reportViewOnly=true&reportContextBar=false&sas-welcome=false42
 
1.9%
https://www.gob.mx/salud/prensa/version-estenografica-conferencia-de-prensa-informe-diario-sobre-coronavirus-covid-19-en-mexico-262965?idiom=es42
 
1.9%
https://www.covid19.admin.ch/en/epidemiologic/vacc-doses40
 
1.8%
https://www.swissinfo.ch/spa/coronavirus-costa-rica_costa-rica-ha-aplicado-57.701-vacunas-contra-la-covid-19/4633671240
 
1.8%
Other values (64)1534
70.5%
2021-02-05T20:20:11.873151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://coronavirus.data.gov.uk/details/healthcare246
 
11.3%
http://english.www.gov.cn/news/topnews/202101/31/content_ws6016a596c6d0f72576944df7.html48
 
2.2%
https://health-infobase.canada.ca/covid-19/vaccination-coverage47
 
2.2%
https://datadashboard.health.gov.il/covid-19/general47
 
2.2%
https://covid.cdc.gov/covid-data-tracker/#vaccinations46
 
2.1%
https://twitter.com/moh_bahrain/status/135707146012951756843
 
2.0%
https://informesdeis.minsal.cl/sasvisualanalytics/?reporturi=%2freports%2freports%2f1a8cc7ff-7df0-474f-a147-929ee45d1900&sectionindex=0&sso_guest=true&reportviewonly=true&reportcontextbar=false&sas-welcome=false42
 
1.9%
https://www.gob.mx/salud/prensa/version-estenografica-conferencia-de-prensa-informe-diario-sobre-coronavirus-covid-19-en-mexico-262965?idiom=es42
 
1.9%
https://www.swissinfo.ch/spa/coronavirus-costa-rica_costa-rica-ha-aplicado-57.701-vacunas-contra-la-covid-19/4633671240
 
1.8%
https://www.covid19.admin.ch/en/epidemiologic/vacc-doses40
 
1.8%
Other values (64)1534
70.5%

Most occurring characters

ValueCountFrequency (%)
t12351
 
7.6%
a11484
 
7.1%
/10681
 
6.6%
s9775
 
6.0%
o9227
 
5.7%
e8925
 
5.5%
i8853
 
5.4%
-7342
 
4.5%
c6804
 
4.2%
n6203
 
3.8%
Other values (59)71193
43.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter119994
73.7%
Other Punctuation18496
 
11.4%
Decimal Number13942
 
8.6%
Dash Punctuation7342
 
4.5%
Uppercase Letter2427
 
1.5%
Connector Punctuation335
 
0.2%
Math Symbol302
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
t12351
 
10.3%
a11484
 
9.6%
s9775
 
8.1%
o9227
 
7.7%
e8925
 
7.4%
i8853
 
7.4%
c6804
 
5.7%
n6203
 
5.2%
r6139
 
5.1%
d5304
 
4.4%
Other values (16)34929
29.1%
ValueCountFrequency (%)
O308
12.7%
C234
 
9.6%
I209
 
8.6%
V189
 
7.8%
A185
 
7.6%
S139
 
5.7%
B129
 
5.3%
F126
 
5.2%
P126
 
5.2%
M117
 
4.8%
Other values (12)665
27.4%
ValueCountFrequency (%)
12574
18.5%
91973
14.2%
01766
12.7%
21382
9.9%
31235
8.9%
61166
8.4%
41103
7.9%
51059
7.6%
7978
 
7.0%
8706
 
5.1%
ValueCountFrequency (%)
/10681
57.7%
.5120
27.7%
:2175
 
11.8%
&210
 
1.1%
%126
 
0.7%
?92
 
0.5%
#85
 
0.5%
,7
 
< 0.1%
ValueCountFrequency (%)
-7342
100.0%
ValueCountFrequency (%)
_335
100.0%
ValueCountFrequency (%)
=302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin122421
75.2%
Common40417
 
24.8%

Most frequent character per script

ValueCountFrequency (%)
t12351
 
10.1%
a11484
 
9.4%
s9775
 
8.0%
o9227
 
7.5%
e8925
 
7.3%
i8853
 
7.2%
c6804
 
5.6%
n6203
 
5.1%
r6139
 
5.0%
d5304
 
4.3%
Other values (38)37356
30.5%
ValueCountFrequency (%)
/10681
26.4%
-7342
18.2%
.5120
12.7%
12574
 
6.4%
:2175
 
5.4%
91973
 
4.9%
01766
 
4.4%
21382
 
3.4%
31235
 
3.1%
61166
 
2.9%
Other values (11)5003
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII162838
100.0%

Most frequent character per block

ValueCountFrequency (%)
t12351
 
7.6%
a11484
 
7.1%
/10681
 
6.6%
s9775
 
6.0%
o9227
 
5.7%
e8925
 
5.5%
i8853
 
5.4%
-7342
 
4.5%
c6804
 
4.2%
n6203
 
3.8%
Other values (59)71193
43.7%

Interactions

2021-02-05T20:19:59.135930image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:19:59.259370image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:19:59.373455image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:19:59.483526image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:19:59.591956image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:19:59.702240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:19:59.866377image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:00.068094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:00.185526image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:00.324804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:00.476153image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:00.608750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:00.710542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:00.857904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:00.983671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:01.083946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:01.183311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:01.286438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:01.383484image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:01.485504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:01.593672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:01.723162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:01.852287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:01.960226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:02.067331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:02.191246image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:02.415965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:02.531376image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:02.640176image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:02.746543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:02.867702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:02.967584image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:03.074090image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:03.182666image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:03.284585image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:03.387876image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:03.494974image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:03.598525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:03.706505image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:03.805949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:03.912697image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:04.014433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:04.111567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:04.210021image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:04.313297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:04.415488image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:04.519081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:04.613766image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:04.713202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:04.821508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:04.926871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:05.032286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:05.150928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:05.259206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:05.364873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:05.465692image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:05.575122image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:05.672404image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:05.764968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:05.859810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:05.955515image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:06.165260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:06.270158image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:06.367731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:06.456945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:06.559483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:06.657002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:06.749943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:06.854750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:06.957786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:07.056710image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-02-05T20:20:07.160229image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-02-05T20:20:11.979932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-05T20:20:12.138073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-05T20:20:12.292294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-05T20:20:12.458512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-05T20:20:12.630882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-05T20:20:07.345457image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-05T20:20:07.658759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-02-05T20:20:07.886165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-02-05T20:20:08.084741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

countryiso_codedatetotal_vaccinationspeople_vaccinatedpeople_fully_vaccinateddaily_vaccinations_rawdaily_vaccinationstotal_vaccinations_per_hundredpeople_vaccinated_per_hundredpeople_fully_vaccinated_per_hundreddaily_vaccinations_per_millionvaccinessource_namesource_website
0AlgeriaDZA2021-01-290.0NaNNaNNaNNaN0.00NaNNaNNaNSputnik VMinistry of Healthhttps://www.aps.dz/regions/116777-blida-covid-19-trente-vaccines-au-matin-du-1er-jour-de-la-campagne
1AlgeriaDZA2021-01-3030.0NaNNaN30.030.00.00NaNNaN1.0Sputnik VMinistry of Healthhttps://www.aps.dz/regions/116777-blida-covid-19-trente-vaccines-au-matin-du-1er-jour-de-la-campagne
2AndorraAND2021-01-25576.0576.0NaNNaNNaN0.750.75NaNNaNPfizer/BioNTechGovernment of Andorrahttps://www.govern.ad/comunicats/item/12379-se-supera-el-miler-de-persones-vacunes-contra-el-coronavirus-sars-cov-2-a-andorra
3AndorraAND2021-01-26NaNNaNNaNNaN66.0NaNNaNNaN854.0Pfizer/BioNTechGovernment of Andorrahttps://www.govern.ad/comunicats/item/12379-se-supera-el-miler-de-persones-vacunes-contra-el-coronavirus-sars-cov-2-a-andorra
4AndorraAND2021-01-27NaNNaNNaNNaN66.0NaNNaNNaN854.0Pfizer/BioNTechGovernment of Andorrahttps://www.govern.ad/comunicats/item/12379-se-supera-el-miler-de-persones-vacunes-contra-el-coronavirus-sars-cov-2-a-andorra
5AndorraAND2021-01-28NaNNaNNaNNaN66.0NaNNaNNaN854.0Pfizer/BioNTechGovernment of Andorrahttps://www.govern.ad/comunicats/item/12379-se-supera-el-miler-de-persones-vacunes-contra-el-coronavirus-sars-cov-2-a-andorra
6AndorraAND2021-01-29NaNNaNNaNNaN66.0NaNNaNNaN854.0Pfizer/BioNTechGovernment of Andorrahttps://www.govern.ad/comunicats/item/12379-se-supera-el-miler-de-persones-vacunes-contra-el-coronavirus-sars-cov-2-a-andorra
7AndorraAND2021-01-30NaNNaNNaNNaN66.0NaNNaNNaN854.0Pfizer/BioNTechGovernment of Andorrahttps://www.govern.ad/comunicats/item/12379-se-supera-el-miler-de-persones-vacunes-contra-el-coronavirus-sars-cov-2-a-andorra
8AndorraAND2021-01-31NaNNaNNaNNaN66.0NaNNaNNaN854.0Pfizer/BioNTechGovernment of Andorrahttps://www.govern.ad/comunicats/item/12379-se-supera-el-miler-de-persones-vacunes-contra-el-coronavirus-sars-cov-2-a-andorra
9AndorraAND2021-02-011036.01036.0NaNNaN66.01.341.34NaN854.0Pfizer/BioNTechGovernment of Andorrahttps://www.govern.ad/comunicats/item/12379-se-supera-el-miler-de-persones-vacunes-contra-el-coronavirus-sars-cov-2-a-andorra

Last rows

countryiso_codedatetotal_vaccinationspeople_vaccinatedpeople_fully_vaccinateddaily_vaccinations_rawdaily_vaccinationstotal_vaccinations_per_hundredpeople_vaccinated_per_hundredpeople_fully_vaccinated_per_hundreddaily_vaccinations_per_millionvaccinessource_namesource_website
2165WalesNaN2021-01-24271376.0270833.0543.06322.017063.08.618.590.025412.0Oxford/AstraZeneca, Pfizer/BioNTechGovernment of the United Kingdomhttps://coronavirus.data.gov.uk/details/healthcare
2166WalesNaN2021-01-25290147.0289566.0581.018771.018279.09.209.180.025798.0Oxford/AstraZeneca, Pfizer/BioNTechGovernment of the United Kingdomhttps://coronavirus.data.gov.uk/details/healthcare
2167WalesNaN2021-01-26312944.0312305.0639.022797.019537.09.939.910.026197.0Oxford/AstraZeneca, Pfizer/BioNTechGovernment of the United Kingdomhttps://coronavirus.data.gov.uk/details/healthcare
2168WalesNaN2021-01-27336745.0336071.0674.023801.020845.010.6810.660.026611.0Oxford/AstraZeneca, Pfizer/BioNTechGovernment of the United Kingdomhttps://coronavirus.data.gov.uk/details/healthcare
2169WalesNaN2021-01-28362970.0362253.0717.026225.021463.011.5111.490.026807.0Oxford/AstraZeneca, Pfizer/BioNTechGovernment of the United Kingdomhttps://coronavirus.data.gov.uk/details/healthcare
2170WalesNaN2021-01-29378950.0378200.0750.015980.019705.012.0212.000.026250.0Oxford/AstraZeneca, Pfizer/BioNTechGovernment of the United Kingdomhttps://coronavirus.data.gov.uk/details/healthcare
2171WalesNaN2021-01-30404249.0403463.0786.025299.019885.012.8212.800.026307.0Oxford/AstraZeneca, Pfizer/BioNTechGovernment of the United Kingdomhttps://coronavirus.data.gov.uk/details/healthcare
2172WalesNaN2021-01-31417147.0416306.0841.012898.020824.013.2313.200.036605.0Oxford/AstraZeneca, Pfizer/BioNTechGovernment of the United Kingdomhttps://coronavirus.data.gov.uk/details/healthcare
2173WalesNaN2021-02-01440706.0439640.01066.023559.021508.013.9813.940.036822.0Oxford/AstraZeneca, Pfizer/BioNTechGovernment of the United Kingdomhttps://coronavirus.data.gov.uk/details/healthcare
2174WalesNaN2021-02-02463657.0462497.01160.022951.021530.014.7114.670.046829.0Oxford/AstraZeneca, Pfizer/BioNTechGovernment of the United Kingdomhttps://coronavirus.data.gov.uk/details/healthcare